We propose UnMixMatch, a semi-supervised learning framework which can learn
effective representations from unconstrained unlabelled data in order to scale
up performance. Most existing semi-supervised methods rely on the assumption
that labelled and unlabelled samples are drawn from the same distribution,
which limits the potential for improvement through the use of free-living
unlabeled data. Consequently, the generalizability and scalability of
semi-supervised learning are often hindered by this assumption. Our method aims
to overcome these constraints and effectively utilize unconstrained unlabelled
data in semi-supervised learning. UnMixMatch consists of three main components:
a supervised learner with hard augmentations that provides strong
regularization, a contrastive consistency regularizer to learn underlying
representations from the unlabelled data, and a self-supervised loss to enhance
the representations that are learnt from the unlabelled data. We perform
extensive experiments on 4 commonly used datasets and demonstrate superior
performance over existing semi-supervised methods with a performance boost of
4.79%. Extensive ablation and sensitivity studies show the effectiveness and
impact of each of the proposed components of our method.

提出了一种名为 UnMixMatch 的半监督学习框架，该框架可以从无约束的未标记数据中学习有效表示，以提高性能。它由三个组件组成：具有硬增强的监督学习器提供强大的正则化，对照一致性正则化器从未标记数据中学习底层表示以及自监督损失用于增强从未标记数据中学习的表示。在 4 个常用数据集上进行了实验，在现有的半监督方法上表现出卓越的性能提高 4.79%。广泛的消融和敏感性研究表明了我们方法的每个提出组件的有效性和影响。

使用无约束未标记数据扩大半监督学习规模

Scaling Up Semi-supervised Learning with Unconstrained Unlabelled Data

In this paper, we explore how to utilize pre-trained language model to
perform few-shot text classification where only a few annotated examples are
given for each class. Since using traditional cross-entropy loss to fine-tune
language model under this scenario causes serious overfitting and leads to
sub-optimal generalization of model, we adopt supervised contrastive learning
on few labeled data and consistency-regularization on vast unlabeled data.
Moreover, we propose a novel contrastive consistency to further boost model
performance and refine sentence representation. After conducting extensive
experiments on four datasets, we demonstrate that our model (FTCC) can
outperform state-of-the-art methods and has better robustness.

本文研究了如何使用预训练的语言模型进行少样本文本分类，提出了一种基于有限标注数据的监督对比学习和大量无标注数据的一致性约束方法，并且提出一种新的对比一致性来改善模型性能。在四个数据集上的实验证明，我们的模型 (FTCC) 可以胜过现有的最佳方法并且具有更好的鲁棒性。